Automated signature creator for a signature based intrusion detection system (Pancakes)

Date of Publication

2013

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Computer Science

Subject Categories

Computer Sciences

College

College of Computer Studies

Department/Unit

Computer Science

Thesis Adviser

Miguel Alberto N. Gomez

Abstract/Summary

A Signature-Based Intrusion Detection System (IDS) helps maintain the integrity of data in a network controlled environment. Unfortunately, this type of IDS depends on predetermined intrusion patterns that are manually created by security professionals, thus manual signature creation is limited by the time allotted by these specialists towards the creation of these signatures. Accordingly, new attacks that are yet to be examined are not detected by the IDS because the signature database is not updated, this now opens up a new door for attackers to penetrate the system. Pancakes is a system capable of pre identifying whether a certain network traffic is a threat or not. Upon determining network threats, the system automatically generates signatures in order to be used as a basis by the Signature-Base IDS for network threat detection. Machine Learning is the concept used for network threat identification. Based on the tests and results, Random Forest was identified as the most beneficial algorithm which helps in determining network attacks. The system is tested in a virtual manner in order for it to not counteract with an organizations' policies. The system is automated thus the data inputted in the system would be manipulated only by the system itself. Hence this system creates signatures that are probably correct while maintaining the goal of keeping the IDS updated with regards to the various network attacks it encounters.

Abstract Format

html

Language

English

Format

Print

Accession Number

TU18004

Shelf Location

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

1 volume (various foliations), illustrations (some color), 28 cm

Keywords

Intrusion detection systems (Computer security)

Embargo Period

4-23-2021

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